How to Explain the Prediction of a Machine Learning Model?

Lilian Weng · 8 years ago

Machine learning models deployed in critical sectors like healthcare, finance, and criminal justice require interpretability so stakeholders can understand and trust their decisions. The article reviews interpretable models such as linear regression, naive Bayes, and decision trees, plus techniques for explaining black-box models including prediction decomposition and local gradient explanation. Increased model interpretability enables organizations to meet regulatory requirements, build user trust, and deploy high-stakes AI systems responsibly in real-world applications.